

'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch
@dataclass
class 					_snake_case		:
			__A  :	torch.Tensor  # [batch_size x 3]
			__A  :	torch.Tensor  # [batch_size x 3]
			__A  :	torch.Tensor  # [batch_size x 3]
			__A  :	torch.Tensor  # [batch_size x 3]
			__A  :	int
			__A  :	int
			__A  :	float
			__A  :	float
			__A  :	Tuple[int]






			def 		UpperCamelCase__							(  self     ):
				assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
				assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
				assert len(self.x.shape     ) == len(self.y.shape     ) == len(self.z.shape     ) == len(self.origin.shape     ) == 2






			def 		UpperCamelCase__							(  self     ):
				return torch.from_numpy(np.array([self.width, self.height]			,dtype=np.floataa     )     )






			def 		UpperCamelCase__							(  self     ):
				return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,dtype=np.floataa     )     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Any						=  torch.arange(self.height * self.width     )
				UpperCAmelCase_     :      Tuple						=  torch.stack(
				    [
				        pixel_indices % self.width,
				        torch.div(_snake_case			,self.width			,rounding_mode="trunc"     ),
				    ]			,axis=1			,)
				return coords






			@property
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					*UpperCAmelCase_     :      Optional[int]						=  self.shape
				UpperCAmelCase_     :      Dict						=  int(np.prod(_snake_case     )     )

				UpperCAmelCase_     :      Optional[int]						=  self.get_image_coords()
				UpperCAmelCase_     :      List[Any]						=  torch.broadcast_to(coords.unsqueeze(0     )			,[batch_size * inner_batch_size, *coords.shape]     )
				UpperCAmelCase_     :      Dict						=  self.get_camera_rays(_snake_case     )

				UpperCAmelCase_     :      int						=  rays.view(_snake_case			,inner_batch_size * self.height * self.width			,2			,3     )

				return rays






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_					,					*UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[Any]						=  coords.shape
				assert n_coords == 2
				assert batch_size == self.origin.shape[0]

				UpperCAmelCase_     :      Dict						=  coords.view(_snake_case			,-1			,2     )

				UpperCAmelCase_     :      Optional[Any]						=  self.resolution()
				UpperCAmelCase_     :      Optional[Any]						=  self.fov()

				UpperCAmelCase_     :      Optional[int]						=  (flat.float() / (res - 1)) * 2 - 1
				UpperCAmelCase_     :      str						=  fracs * torch.tan(fov / 2     )

				UpperCAmelCase_     :      str						=  fracs.view(_snake_case			,-1			,2     )
				UpperCAmelCase_     :      Optional[Any]						=  (
				    self.z.view(_snake_case			,1			,3     )
				    + self.x.view(_snake_case			,1			,3     ) * fracs[:, :, :1]
				    + self.y.view(_snake_case			,1			,3     ) * fracs[:, :, 1:]
				)
				UpperCAmelCase_     :      List[Any]						=  directions / directions.norm(dim=-1			,keepdim=_snake_case     )
				UpperCAmelCase_     :      str						=  torch.stack(
				    [
				        torch.broadcast_to(self.origin.view(_snake_case			,1			,3     )			,[batch_size, directions.shape[1], 3]     ),
				        directions,
				    ]			,dim=2			,)
				return rays.view(_snake_case			,*_snake_case			,2			,3     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				assert width * self.height == height * self.width, "The aspect ratio should not change."
				return DifferentiableProjectiveCamera(
				    origin=self.origin			,x=self.x			,y=self.y			,z=self.z			,width=_snake_case			,height=_snake_case			,x_fov=self.x_fov			,y_fov=self.y_fov			,)




def  a__       (    _SCREAMING_SNAKE_CASE   :       int				)     ->					DifferentiableProjectiveCamera:


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  []
	UpperCAmelCase_     :      Optional[int]						=  []
	UpperCAmelCase_     :      Dict						=  []
	UpperCAmelCase_     :      Union[str, Any]						=  []
	for theta in np.linspace(0	,				2 * np.pi	,				num=20				):
		UpperCAmelCase_     :      Tuple						=  np.array([np.sin(_SCREAMING_SNAKE_CASE				), np.cos(_SCREAMING_SNAKE_CASE				), -0.5]				)
		z /= np.sqrt(np.sum(z**2				)				)
		UpperCAmelCase_     :      Optional[Any]						=  -z * 4
		UpperCAmelCase_     :      List[str]						=  np.array([np.cos(_SCREAMING_SNAKE_CASE				), -np.sin(_SCREAMING_SNAKE_CASE				), 0.0]				)
		UpperCAmelCase_     :      Dict						=  np.cross(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
		origins.append(_SCREAMING_SNAKE_CASE				)
		xs.append(_SCREAMING_SNAKE_CASE				)
		ys.append(_SCREAMING_SNAKE_CASE				)
		zs.append(_SCREAMING_SNAKE_CASE				)
	return DifferentiableProjectiveCamera(
	    origin=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE	,				axis=0				)				).float()	,				x=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE	,				axis=0				)				).float()	,				y=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE	,				axis=0				)				).float()	,				z=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE	,				axis=0				)				).float()	,				width=_SCREAMING_SNAKE_CASE	,				height=_SCREAMING_SNAKE_CASE	,				x_fov=0.7	,				y_fov=0.7	,				shape=(1, len(_SCREAMING_SNAKE_CASE				))	,				)






def a__       (			A_   ):






  '''simple docstring'''





  if len(A_   ) < 2:
    return collection

  def circle_sort_util(A_,	A_,	A_   ) -> bool:
    __magic_name__								=			False

    if low == high:
      return swapped

    __magic_name__								=			low
    __magic_name__								=			high

    while left < right:
      if collection[left] > collection[right]:
        __magic_name__	,       __magic_name__								=			(
            collection[right],
            collection[left],
        )
        __magic_name__								=			True

      left += 1
      right -= 1

    if left == right and collection[left] > collection[right + 1]:
      __magic_name__	,       __magic_name__								=			(
          collection[right + 1],
          collection[left],
      )

      __magic_name__								=			True

    __magic_name__								=			low + int((high - low) / 2   )
    __magic_name__								=			circle_sort_util(A_,	A_,	A_   )
    __magic_name__								=			circle_sort_util(A_,	mid + 1,	A_   )

    return swapped or left_swap or right_swap

  __magic_name__								=			True

  while is_not_sorted is True:
    __magic_name__								=			circle_sort_util(A_,	0,	len(A_   ) - 1   )

  return collection


if __name__ == "__main__":
    __lowerCAmelCase     :	Union[str, Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	Tuple       			=  [int(item) for item in user_input.split(',')]
    print(circle_sort(unsorted))

